Data validation with the pointblank package

This uses pointblank to create a data validation report. In the resulting table at the end, any failing tests should have a CSV button that lets you download a .csv file of just the rows of data that don’t pass that particular validation step.

Check for missing values: Height

Action levels

For most tests, the following criteria are used:

• ‘warn’: 1 or more rows fail conditions
• ‘error’: 2% or more of rows fail conditions.

The exceptions are, —-, —-, —–. These use the strict condition, which will return an ‘error’ if any rows fail.

al_default <-  action_levels(warn_at = 1, stop_at = 0.02) #warn if even row fails, error if 2% of rows fail
al_strict <- action_levels(stop_at = 1) #error if even one row fails

Data Validation

These are evaluations of (1) data type, (2) the range of values for shoots, height, and infloresences, and (3) potential duplicates in HDP_plots.csv and HDP_1997_2009.csv.

Pointblank Validation
Data Validation tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	1	col_vals_in_set()	▮subplot	A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, D1, D2, D3, D4, D5, D6, D7, D8, D9, D10, E1, E2, E3, E4, E5, E6, E7, E8, E9, E10, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, G1, G2, G3, G4, G5, G6, G7, G8, G9, G10, H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J1, J2, J3, J4, J5, J6, J7, J8, J9, J10		✓	67K	67K 1.00	0 0.00	—	○	—	—
	2	col_vals_in_set()	▮plot_id	CF-1, CF-2, CF-3, CF-4, CF-5, CF-6, FF-1, FF-2, FF-3, FF-4, FF-5, FF-6, FF-7		✓	67K	67K 1.00	0 0.00	—	○	—	—
	3	Height is measured to nearest cm col_vals_expr()	—	ht%%1 == 0		✓	57K	57K 1.00	0 0.00	—	○	—	—
	4	Shoots is interger col_vals_expr()	—	shts%%1 == 0		✓	57K	57K 1.00	0 0.00	—	○	—	—
	5	Number of inflorescences is integer col_vals_expr()	—	infl%%1 == 0		✓	2K	2K 1.00	0 0.00	—	○	—	—
	6	shoots between 0 and 20 col_vals_between()	▮shts	[0, 20]		✓	67K	67K 0.99	8 0.01	●	○	—	CSV
	7	height between 0 and 200cm col_vals_between()	▮ht	[0, 200]		✓	67K	67K 0.99	2 0.01	●	○	—	CSV
	8	infloresences between 0 and 3 col_vals_between()	▮infl	[0, 3]		✓	67K	67K 0.99	15 0.01	●	○	—	CSV
	9	duplicated rows rows_distinct()	—	—		✓	67K	67K 1.00	0 0.00	—	○	—	—
	10	col_vals_not_null()	▮plant_id	—		✓	67K	67K 1.00	0 0.00	—	○	—	—
	11	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	3K	3K 1.00	0 0.00	—	○	—	—
	12	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	4K	4K 1.00	0 0.00	—	○	—	—
	13	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	5K	5K 1.00	0 0.00	—	○	—	—
	14	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	15	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	16	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	17	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	18	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	19	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	7K	7K 1.00	0 0.00	—	○	—	—
	20	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	5K	5K 1.00	0 0.00	—	○	—	—
	21	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
	22	Check for duplicate ID's within each year rows_distinct()	▮plant_id	—		✓	6K	6K 1.00	0 0.00	—	○	—	—
2023-05-25 20:49:58 EDT 2.6 s 2023-05-25 20:50:00 EDT

Plant Size

This test is for unusually large changes in plant size from one year to the next.

Checks that year to year change in size is reasonable

Pointblank Validation
Check growth & regression tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	1	|% change in height| < 200% col_vals_lt()	▮ht_pc	2		✓	67K	66K 0.99	420 0.01	●	○	—	CSV
	2	|∆ height| < 100cm col_vals_between()	▮ht_diff	[−100, 100]		✓	67K	67K 0.99	11 0.01	—	●	—	CSV
	3	|∆ shoot number| < 5 col_vals_between()	▮shts_diff	[−5, 5]		✓	67K	67K 0.99	201 0.01	—	●	—	CSV
2023-05-25 20:50:02 EDT < 1 s 2023-05-25 20:50:02 EDT

Seedling Size

This searches for seedlings that are unusually large.

Pointblank Validation
Check seedlings tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
	1	shoots < 3 col_vals_lt()	▮shts	3		✓	3K	3K 0.99	12 0.01	●	○	—	CSV
	2	height < 30cm col_vals_lt()	▮ht	30		✓	3K	3K 0.99	3 0.01	●	○	—	CSV
2023-05-25 20:50:03 EDT < 1 s 2023-05-25 20:50:03 EDT